Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.
|Depends:||R (≥ 3.2)|
|Imports:||NetPreProc (≥ 1.1), Rcpp (≥ 0.12.2), caret (≥ 6.0-52), proxy (≥ 0.4-15), xgboost (≥ 0.4), klaR (≥ 0.6-12), e1071 (≥ 1.6-7), stats (≥ 3.2)|
|Maintainer:||Junxiang Wang <xianggebenben at 163.com>|
|License:||GPL (≥ 3)|
|CRAN checks:||SSL results|
|Windows binaries:||r-devel: SSL_0.1.zip, r-release: SSL_0.1.zip, r-oldrel: SSL_0.1.zip|
|OS X El Capitan binaries:||r-release: not available|
|OS X Mavericks binaries:||r-oldrel: SSL_0.1.tgz|
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